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Theme issue: Convergence of Federated Learning and HPC for 6G assisted Wireless Communication Systems [CFP]

Personal and Ubiquitous Computing
Peter Thomas
Personal and Ubiquitous Computing
springer.com/journal/779
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Guest Editors

Chi Lin Dalian University of Technology, Dalian, China
clindut@ieee.org
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Chang Wu Yu, Chung Hua University, Hsinchu, Taiwan
cwyu@chu.edu.tw
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Ning Wang Rowan University, Glassboro, New Jersey, USA
wangn@rowan.edu
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Wireless communication has the potential to transform our lives - from enabling breakthroughs in scientific innovations to more effective personal communication.
The advent of 6G networks will plays a big role in creating the next generation of technology infrastructure and the convergence of technologies such as federated learning and High-Performance Computing (HPC) will being numerous benefits and industries are reinventing their practices to take advantage of the potential of 6G networks.
From chatbots to smart homes to robotic vehicles, advances in 6G has brought about an increasing demand for HPC and federated learning and also integrates technologies such as the Industrial Internet of Things (IIoT), Artificial Intelligence (AI), and cloud computing.
HPC and federated learning are interlinked and their convergence will drive innovations in 6G assisted wireless communication systems for many enterprises across many verticals. This convergence will not only make the networks smarter and faster, but also provide more accurate results and network management.

Scope

Topics of interest include but are not restricted to:
Advances in federated learning and HPC for 6G wireless networks
HPC and federated learning unlocking the true potential of 6G networks
Leveraging HPC and federated learning to drive innovations in 6G assisted wireless networks
Federated HPC technology for 6G assisted applications
Challenges, applications, and opportunities of converging federated learning and HPC for 6G networks
Mobile network-based wireless modulation in 6G using federated learning and HPC
Applications of 6G assisted federated learning for HPC based critical applications
Federated learning meets HPC and 6G at the edge for future generation wireless communication systems
Edge/IoT assisted 6G networks using federated learning
HPC assisted federated optimisation for 6G networks
Secure spectrum allocation in 6G networks using federated learning and HPC

Important Dates

Submissions: 25th October, 2022
Notification: 20th February, 2023
Revised Versions: 15th April, 2023
Final Decisions: 27th July, 2023

Submissions

Submissions should be original papers and should not be under consideration for publication elsewhere. Extended versions of high-quality conference papers that are already published at relevant venues may also be considered as long as the additional contribution is substantial (at least 30% new content).
Authors should follow the formatting and submission instructions for Personal and Ubiquitous Computing at
https://www.springer.com/journal/779
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For more information visit the Springer Nature Information for journal Article Authors pages at
https://www.springer.com/gp/authors-editors/journal-author
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During the first submission step in Editorial Manager select Original article as the article type. In further steps you should confirm that your submission belongs to this special issue by choosing the special issue title from the drop-down menu.
All papers will be peer-reviewed. Before any special issue is given final approval to be put into production, additional rigorous integrity checks are carried out by the editor-in-chief, editorial team, production office and by Springer Nature.


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